Stochastic Scene-Aware Motion Prediction [Project Page][Paper] Description This repository contains the training code for MotionNet and GoalNet of SAMP. Installation To install the necessary dependencies run the following command: pip install -r requirements.txt The code has been tested with Python 3.8.10, CUDA 10.0, CuDNN 7.5 and PyTorch 1.7.1 on Ubuntu 20.04. Training Data The training data for MotionNet and GoalNet could be found in the website downloads. Or could be extractedfrom the Unity runtime code. Update    

This repository contains the code and data for the paper “Hand-Object Contact Prediction via Motion-Based Pseudo-Labeling and Guided Progressive Label Correction” by Takuma Yagi, Md. Tasnimul Hasan and Yoichi Sato. Requirements Python 3.6+ ffmpeg numpy opencv-python pillow scikit-learn python-Levenshtein pycocotools torch (1.8.1, 1.4.0- for flow generation) torchvision (0.9.1) mllogger flownet2-pytorch Caution: This repository requires ~100GB space for testing, ~200GB space for trusted label training and ~3TB space for full training. Getting Started Download the data Download EPIC-KITCHENS-100 videos from the […]

This repository contains the official chumpy implementation of mocap body solver used for AMASS: AMASS: Archive of Motion Capture as Surface ShapesNaureen Mahmood, Nima Ghorbani, Nikolaus F. Troje, Gerard Pons-Moll, Michael J. BlackFull paper |Video |Project website |Poster Description This repository holds the code for MoSh++, introduced in AMASS, ICCV’19.MoSh++ is the upgraded version of MoSh, Sig.Asia’2014.Given a labeled marker-based motion capture (mocap) c3d file and the correspondencesof the marker labels to the locations on the body, MoSh canreturn model […]